237
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Conceptual cost estimation of highway bid unit prices using ordinary kriging

, &

References

  • Adhikary PP, Dash CJ. 2017. Comparison of deterministic and stochastic methods to predict the spatial variation of groundwater depth. Appl Water Sci. 7(1):339–348.
  • American Association of State and Highway Transportation Officials (AASHTO). 2013. Practical guide to cost estimating. Washington (DC): AASHTO.
  • Anderson S, Molenaar K, Schexnayder C. 2007. Guidance for cost estimation and management for highway projects during planning, programming and preconstruction. NCHRP Rep. 574. Washington (DC): National Cooperative Highway Research Program (NCHRP), Transportation Research Board of the National Academics.
  • Antoniou F, Konstantinidis D, Aretoulis G, Xenidis Y. 2018. Preliminary construction cost estimates for motorway underpass bridges. Int J Constr Manage. 18(4):321–330.
  • Asa E, Saafi M, Membah J, Billa A. 2012. Comparison of linear and nonlinear kriging methods for characterization and interpolation of soil data. J Comput Civ Eng. 26(1):11–18.
  • Asmar ME, Hanna AS, Whited GC. 2011. A new approach to developing conceptual cost estimates for highway projects. J Constr Eng Manage. 137(11):942–949.
  • Baek M, Ashuri B. 2017. Spatial regression analysis for modelling the spatial variation in highway construction costs. In: Resilient structures and sustainable construction. Atlanta (GA): Georgia Institute of Technology.
  • Baek M, Ashuri B. 2019. Analysis of the variability of submitted unit price bids for asphalt line items in highway projects. J Constr Eng Manage. 145(4):04019020.
  • Bansal VK. 2020. Use of GIS to consider spatial aspects in the construction planning process. Int J Constr Manage. 20(3):207–222.
  • Cao Y, Ashuri B, Baek M. 2018. Prediction of unit price bids of resurfacing highway projects through ensemble machine learning. J Comput Civ Eng. 32(5):1–10.
  • Cattell DW, Bowen PA, Kaka AP. 2007. Review of unbalanced bidding models in construction. J Constr Eng Manage. 133(8):562–573.
  • Chai T, Draxler RR. 2014. Root mean square error (RMSE) or mean absolute error (MAE)? – arguments against avoiding RMSE in the literature. Geosci Model Dev. 7(3):1247–1250.
  • Chiles JP, Delfiner P. 2009. Geostatistics: modeling spatial uncertainty. Vol. 497. John Wiley & Sons.
  • Choi CY, Ryu KR, Shahandashti M. 2021. Predicting city-level construction cost index using linear forecasting models. J Constr Eng Manage. 147(2):04020158
  • Choi S, Kim DY, Han SH, Kwak YH. 2014. Conceptual cost-prediction model for public road planning via rough set theory and case-based reasoning. J Constr Eng Manage. 140(1):04013026.
  • Eldeiry A. A, Garcia LA. 2012. Evaluating the performance of ordinary kriging in mapping soil salinity. J Irrig Drain, 138(12):1046–1059.
  • ESRI. 2016. ESRI Inc. https://desktop.arcgis.com/en/arcmap/10 .3/tools/3d-analyst-toolbox/how-idw-works.htm.
  • ESRI. 2020. ESRI. https://desktop.arcgis.com/en/arcmap/latest/tools/spatial-statistics-toolbox/h-how-spatial-autocorrelation-moran-s-i-spatial-st.htm.
  • Gardner BJ, Gransberg DD, Jeong HD. 2016. Reducing data-collection efforts for conceptual cost estimating at a highway agency. J Constr Eng Manage. 142(11):04016057.
  • Gardner BJ, Gransberg DD, Rueda JA. 2017. Stochastic conceptual cost estimating of highway projects to communicate uncertainty using bootstrap sampling. ASCE-ASME J Risk Uncertainty Eng Syst, Part A: Civ Eng. 3(3):1–9.
  • Gransberg DD, Riemer C. 2009. Impact of inaccurate engineer’s estimated quantities on unit price contracts. J Constr Eng Manage. 135(11):1138–1145.
  • Gupta A, Kamble T, Machiwal D. 2017. Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of northwest India. Environ Earth Sci. 76(15):1–16.
  • He X, Liu R, Anumba CJ. 2021. Data-driven insights on the knowledge gaps of conceptual cost estimation modeling. J Constr Eng Manage. 147(2):04020165.
  • Krivoruchko K. 2011. Spatial statistical data analysis for GIS users. Redlands: Esri Press.
  • Le C, Le T, Jeong HD, Lee EB. 2019. Geographic information system-based framework for estimating and visualizing unit prices of highway work items. J Constr Eng Manage, 145(8), 1–12.
  • Martinez A. 2010. Validation of methods for adjusting construction cost estimates by project location. Albuquerque (NM): University of New Mexico.
  • Migliaccio GC, Guindani M, D'Incognito M, Zhang L. 2013. An empirical assessment of spatial prediction methods for location cost-adjustment factors. J Constr Eng Manag. 139(7):858–869.
  • Migliaccio GC, Zandbergen P, Martinez AA. 2009. Assessment of methods for adjusting construction cost estimates by geographical location. 2009 Construction Research Congress: Building a Sustainable Future. p. 886–895.
  • Oliver MA, Webster R. 2015. Basic steps in geostatistics: the variogram and kriging. Cham, Switzerland: Springer International Publishing.
  • Pang W, Liu F, Fang S, Yue L. 2012, June. Spatial correlation and wind speed uncertainties of hurricane wind field model. In 2012 Joint Conference of the Engineering Mechanics Institute and the 11th ASCE Joint Specialty Conference on Probabilistic Mechanics and Structural Reliability.
  • Persons TM. 2020. Cost estimating and assessment guide: best practices for developing and managing program costs. Washington (DC): US Government Accountability Office, GAO-20-195G.
  • Polat G, Turkoglu H, Damci A. 2018. Detection of unbalanced bids: a case study. In: Creative Construction Conference 2018. Budapest University of Technology and Economics; pp. 432–439.
  • Robinson TP, Metternicht G. 2006. Testing the performance of spatial interpolation techniques for mapping soil properties. Comput Electron Agric. 50(2):97–108.
  • RSMeans. 2020. The Map Changes Everything. https://www.Rsmeans.com/landing-pages/cost-map.
  • Shamo B, Asa E, Membah J. 2015. Linear spatial interpolation and analysis of annual average daily traffic data. J Comput Civ Eng. 29(1):1–8.
  • Shresthaa KJ, Jeong HD. 2019. Automated unit price visualization using ArcPy site package in ArcGIS. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, Vol. 36, 57–61. IAARC Publications.
  • Sonmez R. 2011. Range estimation of construction costs using neural networks with bootstrap prediction intervals. Expert Syst Appl. 38(8):9913–9917.
  • Wisconsin Department of Transportation (WisDOT). 2010. Understanding the WisDOT chained fisher construction cost index. https://wisconsindot.gov/Documents/doing-bus/eng-consultants/cnsltrsrces/tools/estimating/understanding-the-cci.pdf.
  • Wisconsin Department of Transportation (WisDOT). 2020. Facilities development manual. https://wisconsindot.gov/rdwy/fdm/fd-19-05.pdf#fd19-5-5.3.7.
  • Wu CY, Mossa J, Mao L, Almulla M. 2019. Comparison of different spatial interpolation methods for historical hydrographic data of the lowermost Mississippi River. Ann Gis. 25(2):133–151.
  • Zhang S, Bogus SM, Lippitt CD, Migliaccio GC. 2017. Estimating location-adjustment factors for conceptual cost estimating based on nighttime light satellite imagery. J Constr Eng Manage. 143(1):1–9.
  • Zhang S, Migliaccio GC, Zandbergen PA, Guindani M. 2014. An empirical assessment of geographically based surface interpolation methods for adjusting construction cost estimates by project location. J Constr Eng Manage. 140(6):1–13.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.